Joint Director’s Special Colloquium & Physics Colloquium: ‘Deep Learning Theory and Applications in Natural Sciences’
Pierre Baldi, a Chancellor’s Professor in the Department of Computer Science at the University of California, Irvine and the Director of its Institute for Genomics & Bioinformatics, will present “Deep Learning Theory and Applications in Natural Sciences” at a Joint Director’s Special Colloquium & Physics Colloquium.
The event takes place Friday, April 28, 2017, at 11 a.m. in the Bldg. 203 Auditorium. All employees whose schedules permit are invited to attend.
Shuttle service will be provided starting at 10:15 a.m. with stops at Bldgs. 362, 401, 212 to 203. Return trips will follow the talk.
The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning — essentially learning in complex systems comprised of multiple processing stages — is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. Deep learning systems are now deployed ubiquitously and used by billions of people every day for instance through cell phones and web search engines.
We will first provide a brief historical overview of artificial neural networks and deep learning, starting from their early origins in the 1940s and their connections to biological neural networks and learning, and ending with examples of some of the most recent successes in engineering applications. While we do not yet have a comprehensive theory of deep learning, we will also provide a brief overview of a growing body of theoretical results about deep learning highlighting some of the remaining gaps and open questions in the field. We will then present various applications of deep learning to problems in the natural sciences, such as the detection of exotic particles in high-energy physics, the prediction of molecular properties and reactions in chemistry and the prediction of protein structures in biology.
Pierre Baldi is a Chancellor’s Professor in the Department of Computer Science, School of Information and Computer Sciences, and Director of the Institute for Genomics & Bioinformatics at the University of California, Irvine. His work centers on artificial intelligence and machine learning with particular emphasis on deep learning, neural networks, reinforcement earning, and their theoretical foundations and applications. The driving interest behind his efforts is to understand natural and artificial intelligence. Baldi received his Ph.D. in Mathematics from the California Institute of Technology.